Augmenting Dual Decomposition for MAP Inference

author: André F. T. Martins, Language Technologies Institute, Carnegie Mellon University
published: Jan. 13, 2011,   recorded: December 2010,   views: 351
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Description

In this paper, we propose combining augmented Lagrangian optimization with the dual decomposition method to obtain a fast algorithm for approximate MAP (maximum a posteriori) inference on factor graphs. We also show how the proposed algorithm can efficiently handle problems with (possibly global) structural constraints. The experimental results reported testify for the state-of-the-art performance of the proposed approach.

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